基于核主元分析与支持向量机的监控诊断方法及其应用

为了及时反映密闭鼓风炉冶炼过程状态,实现对密闭鼓风炉炉况的监控与诊断,提出核主元分析和多支持向量机分类的相结合的过程监控与故障诊断方法.其原理是:首先,用核主元分析方法提取过程数据特征,建立核主元分析的监控模型;然后,将代表过程特征的核主元送入多支持向量机分类器中,利用"一对其余"算法对故障进行诊断与分类.实验结果表明,所提出的方法与传统的主元分析方法相比,整个样本集的可分性变大,分类正确率提高,能更准确地诊断炉子的各种故障,可有效地用于密闭鼓风炉冶炼过程的故障诊断.     

基于重构贡献的双容水箱故障诊断方法

针对流程工业过程中变量数据众多,变量间耦合性强,常规主元分析法对于故障数据的分离存在一定局限性,提出了基于主元分析和重构贡献的双容水箱系统故障检测与诊断方法,介绍了一种基于SPE和T2统计量的综合评价指标,定义了重构贡献统计量,分析了重构贡献法的故障诊断性能,利用重构贡献法与传统贡献法进行对比诊断实验研究,并验证了重构贡献法故障诊断的有效性。     

基于ICA-PCA和Lasso的过程故障诊断

为了解决复杂工业过程中变量多,难以判断引起故障的主要异常变量的问题,提出一种基于ICA-PCA(独立成分分析和主成分分析)算法和Lasso(最小绝对收缩和选择算子)回归算法的过程故障检测与诊断的集成模型.首先,建立ICA-PCA模型提取数据的高斯信号和非高斯信号,构造相关统计量实现在线故障检测;然后,基于ICA-PCA模型获得的过程状态及故障信息,进一步构造基于Lasso回归算法的故障诊断模型,实现故障发生时的主要异常变量的定位和选择;最后,利用Matlab进行了TE(田纳西-伊斯曼)过程的数值仿真实验,并与已有故障诊断方法分布式PCA贡献图法进行比较,结果表明所提出的方法是有效的.     

一种主元分析方法在聚合生产过程故障监测与诊断中的应用

对聚合生产过程中的操作变量进行主元分析，提取过程变量的主要特征，建立主元模型，可实现对多变量过程数据的压缩．在不减少原信息主要特征的基础上，有效地去除了测量噪声．将主元分析应用到聚合反应生产过程故障诊断系统中，大大减少了监测变量个数，能够实现对过程状态的有效监控；异常状态可根据变量对统计量贡献值的大小来确定故障源．     

基于小波包变换-主元分析-神经网络算法的多电平逆变器故障诊断

针对级联多电平逆变器的故障诊断问题,提出了一种基于小波包变换-主元分析-神经网络算法的故障诊断方法。首先,通过小波包变换对故障信号进行多尺度小波包分解,并重构提取小波包能量作为故障特征向量;然后,采用主元分析方法提取主元信息以降低故障特征的维数;最后,利用BP神经网络进行故障分类和识别。通过仿真实验,验证了该方法相对于传统方法具有更高的故障诊断和故障识别的准确率,并且对噪声具有较好的鲁棒性。     

基于CHMM的TE过程在线故障检测

随着工业过程的规模和复杂程度的增加,对于过程安全性和可靠性的要求进一步提高.为了准确及时地检测设备故障,提出了一种基于连续隐马尔可夫模型(CHMM)的在线故障检测方法.采用主元分析(PCA)方法对过程变量数据进行特征提取,利用变长度滑动窗口技术跟踪动态数据,并提出了一个新的实时统计量作为在线故障检测的量化指标,结合实时阈值实现了CHMM的在线故障检测.将该方法应用于田纳西-伊斯曼(TE)化工过程,并与基于PCA和动态主元分析(DPCA)方法的故障检测结果进行比较,能够较准确地检测到故障,验证了该方法的有效性.     

多尺度排列熵在涡旋压缩机故障诊断中的应用

针对传统的单尺度信号分析在故障特征的表达上有很大的局限性,引入多尺度排列熵,对不同尺度下的涡旋压缩机振动信号进行了定量分析,并通过马氏距离实施故障诊断.实验结果表明,与单尺度排列熵相比,多尺度排列熵能够对不同故障的复杂性进行准确的描述,反映了不同尺度下故障特征的变化趋势.此外,选择合适尺度下的排列熵作为故障诊断的特征参数,既节省了时间,又可以提高诊断的准确率.     

虚拟现实变电站主变压器低压侧故障诊断模型研究

传统主变压器低压侧故障诊断模型需要大量故障特征数据;且故障特征数据不相互独立,很难找到最合适的特征实现故障诊断。为此,提出一种新的虚拟现实变电站主变压器低压侧故障诊断模型。介绍了虚拟现实变电站主变压器低压侧典型故障,主要包括漏电故障和短路故障,对这两种故障进行分析,发现出现故障时电压电阻变化特征,为建立故障诊断模型提供依据。依据贝叶斯定理,利用某对象的先验概率,通过贝叶斯公式求出其后验概率,构建单层贝叶斯分类器。在此基础上,利用径向基神经网络将故障类变量看作属性变量的父节点,构建故障特征相互独立的虚拟现实变电站主变压器低压侧故障诊断模型。实验结果表明,构建模型能够准确诊断低压侧故障,具有很高的应用性。     

基于注意力机制BiLSTM的设备智能故障诊断方法

状态监测与故障诊断是保证机械设备安全稳定运行的必要手段.本文提出一种基于注意力机制双向LSTM网络(ABiLSTM)的深度学习框架用于机械设备智能故障诊断.首先,将传感器采集的设备原始数据进行预处理,并划分为训练样本集与测试样本集;其次,训练多个不同尺度的双向LSTM网络对原始时域信号进行特征提取,得到设备故障多尺度特征;再次,通过引入注意力机制,对不同双向LSTM网络提取特征的权重参数进行优化,筛选保留目标特征,滤除冗杂特征,以实现精准提取有效故障特征;最后,在输出端利用Softmax分类器输出故障分类结果.通过利用发动机气缸振动实验数据和凯斯西储大学滚动轴承实验数据进行故障诊断实验,故障识别准确率均达到99%以上.实验结果表明,ABiLSTM模型可以实现对原始时域信号的多尺度特征提取和故障诊断,通过与深度卷积网络(CNN)、深度去噪自编码器(DAE)和支持向量机(SVM)等方法进行对比,ABiLSTM模型的故障识别性能优于各类常见模型.另外,通过利用凯斯西储大学滚动轴承在不同工况条件下的数据,对ABiLSTM模型进行泛化性能实验,变工况样本的故障识别准确率仍然能够达到95%以上.     

虚拟现实变电站主变压器低压侧故障诊断模型

传统主变压器低压侧故障诊断模型需要大量故障特征数据;且故障特征数据不相互独立,很难找到最合适的特征实现故障诊断。为此,提出一种新的虚拟现实变电站主变压器低压侧故障诊断模型。介绍了虚拟现实变电站主变压器低压侧典型故障,主要包括漏电故障和短路故障,对这两种故障进行分析,发现出现故障时电压电阻变化特征,为建立故障诊断模型提供依据。依据贝叶斯定理,利用某对象的先验概率,通过贝叶斯公式求出其后验概率,构建单层贝叶斯分类器。在此基础上,利用径向基神经网络将故障类变量看作属性变量的父节点,构建故障特征相互独立的虚拟现实变电站主变压器低压侧故障诊断模型。实验结果表明,构建模型能够准确诊断低压侧故障,具有很高的应用性。     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

Exciton Seebeck in Molecular Systems

Various applications relevant to the exciton dynamics,such as the organic solar cell,the large-area organic light-emitting diodes and the thermoelectricity,are operating under temperature gradient.The potential abnormal behavior of the exicton dynamics driven by the temperature difference may affect the efficiency and performance of the corresponding devices.In the above situations,the exciton dynamics under temperature difference is mixed with     

Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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An on-line scaling method for improving scalability of a database cluster

The explosive growth of the Internet and database applications has driven database to be more scalable and available, and able to support on-line scaling without interrupting service. To support more client's queries without downtime and degrading the response time, more nodes have to be scaled up while the database is running. This paper presents the overview of scalable and available database that satisfies the above characteristics. And we propose a novel on-line scaling method. Our method improves the existing on-line scaling method for fast response time and higher throughputs. Our proposed method reduces unnecessary network use, i.e. , we decrease the number of data copy by reusing the backup data. Also, our on-line scaling operation can be processed parallel by selecting adequate nodes as new node. Our performance study shows that our method results in significant reduction in data copy time.     

An improved R-tree based on childnode''s probability

R-Tree is a good structure for spatial searching. But in this indexing structure,either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R-tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R-tree (PBAR-tree).     

Integrating GIS Web services based on mediating architecture

The geographic information service is enabled by the advancements in general Web service technology and the focused efforts of the OGC in defining XML-based Web GIS service. Based on these models, this paper addresses the issue of services chaining,the process of combining or pipelining results from several interoperable GIS Web Services to create a customized solution. This paper presents a mediated chaining architecture in which a specific service takes responsibility for performing the process that describes a service chain. We designed the Spatial Information Process Language (SIPL) for dynamic modeling and describing the service chain, also a prototype of the Spatial Information Process Execution Engine (SIPEE) is implemented for executing processes written in SIPL. Discussion of measures to improve the functionality and performance of such system will be included.